SwinSegFormer: Advancing Aerial Image Semantic Segmentation for Flood Detection

SwinSegFormer: Advancing Aerial Image Semantic Segmentation for Flood Detection

Semantic segmentation of aerial images is essential for unmanned aerial vehicle (UAV) applications in disaster management, particularly for identifying the flood-affected areas. Traditional techniques face challenges in capturing global semantic information due to their limited receptive fields, and...

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Bibliographic Details
Main Authors: Muhammad Tariq Shaheen, Hafsa Iqbal, Numan Khurshid, Haleema Sadia, Nasir Saeed
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Open Journal of the Computer Society
Subjects:
Flood detection
semantic segmentation
SegFormer
swin transformer
unmanned aerial vehicles (UAVs)
vision transformers
Online Access:https://ieeexplore.ieee.org/document/10979465/
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Summary:Semantic segmentation of aerial images is essential for unmanned aerial vehicle (UAV) applications in disaster management, particularly for identifying the flood-affected areas. Traditional techniques face challenges in capturing global semantic information due to their limited receptive fields, and high computational requirement. To address these issues, we propose a novel transformer-based model named SwinSegFormer, which feature a hierarchical encoder that efficiently generates multi-scale high-resolution features along with a lightweight decoder to reduce computational overhead. The proposed model is trained on FloodNet dataset and demonstrates efficient performance on challenging classes such as vehicles, pools, and flooded and non-flooded roads, which are crucial for effective disaster management. Additionally, we developed a post-processing module to categorize areas into flooded and non-flooded. The model achieves a validation mIoU of 75.1&#x0025;, mDice of 85.4&#x0025;, and mACC of 87.1&#x0025;, representing a 10-12&#x0025; improvement over state-of-the-art vision transformer-based methods. The effectiveness of model is further evaluated on real-world unlabeled flood imagery, highlighting its potential for supporting first aid activities during floods. Relevant codes are available at: <uri>https://github.com/Shaheen1998/SwinSegFormer</uri>.
ISSN:2644-1268

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